HARMONIC: Cognitive and Control Collaboration in Human-Robotic Teams

This paper presents a novel approach to multi-robot planning and collaboration. We demonstrate a cognitive strategy for robots in human-robot teams that incorporates metacognition, natural language communication, and explainability. The system is embodied using the HARMONIC architecture that flexibly integrates cognitive and control capabilities across the team. We evaluate our approach through simulation experiments involving a joint search task by a team of heterogeneous robots (a UGV and a drone) and a human. We detail the system's handling of complex, real-world scenarios, effective action coordination between robots with different capabilities, and natural human-robot communication. This work demonstrates that the robots' ability to reason about plans, goals, and attitudes, and to provide explanations for actions and decisions are essential prerequisites for realistic human-robot teaming.

HARMONIC: A Framework for Explanatory Cognitive Robots

We present HARMONIC, a framework for implementing cognitive robots that transforms general-purpose robots into trusted teammates capable of complex decision-making, natural communication and human-level explanation. The framework supports interoperability between a strategic (cognitive) layer for high-level decision-making and a tactical (robot) layer for low-level control and execution. We describe the core features of the framework and our initial implementation, in which HARMONIC was deployed on a simulated UGV and drone involved in a multi-robot search and retrieval task.

Automating Knowledge Acquisition for Content-Centric Cognitive Agents Using LLMs

The paper describes a system that uses large language model (LLM) technology to support the automatic learning of new entries in an intelligent agent's semantic lexicon. The process is bootstrapped by an existing non-toy lexicon and a natural language generator that converts formal, ontologically-grounded representations of meaning into natural language sentences. The learning method involves a sequence of LLM requests and includes an automatic quality control step. To date, this learning method has been applied to learning multiword expressions whose meanings are equivalent to those of transitive verbs in the agent's lexicon. The experiment demonstrates the benefits of a hybrid learning architecture that integrates knowledge-based methods and resources with both traditional data analytics and LLMs.

Knowledge Engineering in the Long Game of Artificial Intelligence: The Case of Speech Acts

This paper describes principles and practices of knowledge engineering that enable the development of holistic language-endowed intelligent agents that can function across domains and applications, as well as expand their ontological and lexical knowledge through lifelong learning. For illustration, we focus on dialog act modeling, a task that has been widely pursued in linguistics, cognitive modeling, and statistical natural language processing. We describe an integrative approach grounded in the OntoAgent knowledge-centric cognitive architecture and highlight the limitations of past approaches that isolate dialog from other agent functionalities.